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The Gini coefficient is a commonly used measure that varies between ‘0’ reflecting complete equality and ‘1’ indicating complete inequality. The Gini coefficient is based on the Lorenz curve which compares income across the entire population of an area. It is a useful measure because it incorporates all of the information available from a particular area.

The Gini coefficient of inequality, using consumption expenditures per capita, is presented in Table 3.5. The national Gini coefficient is estimated at 0.445. This reflects a high level of inequality. When we look at the Gini coefficient within rural areas, it is 0.361. In urban areas, it is 0.368. Thus while inequality in urban areas appear similar to that of rural areas, rural areas have a disproportionately higher population at 68.8 percent (compared to 31.2 percent in urban areas) but control only 45.4 percent of the consumption expenditure. Once we bring them together, however, the inequality levels jump. The leap in national Gini coefficient is due to income gaps between rural and urban areas. A decrease in the income gap between urban and rural areas is therefore a necessary condition to reduce the national Gini coefficient. This is highlighted in the consumption expenditure patterns of rural and urban areas that reflect the prevailing income gaps between the populations in the two areas.

The Lorenz curve in Figure 3.3 compares inequality between rural and urban areas between counties. Rural areas show slightly higher levels of inequality than urban areas in counties. This can be explained by the weight of counties with high differences in inequality between their urban and rural areas.

Figure 3.3: The Lorenz Curve

Counties with the highest inequalities are Tana River, Kwale and Kilifi with Gini coefficients of 0.617, 0.597 and 0.565 respectively (Table 3.5). These are situated along the coastal area of the country as seen in Figure 3.4. The most equal counties are Turkana, Narok, and West Pokot with Gini coefficients of 0.283, 0.315, and 0.318, respectively. This data shows that high poverty is not equivalent to high inequality, as the most equal counties, such as Turkana, are also among the poorest.

Figure 3.4: Gini coefficient by county

Table 3.5: Gini coefficient: national, counties and constituencies

NATIONAL

Name

Pop. Share

Mean

Consump. Share

Gini

Kenya

1

3,440

1

0.445

Rural

0.688

2,270

0.454

0.361

Urban

0.312

6,010

0.546

0.368

COUNTIES

CONSTITUENCIES

County

Pop. Share

Mean

Consump. Share

Gini

Constituency

Pop. Share

Mean

Consump. Share

Gini

Top 10 counties

Top 10 constituencies

Tana River

0.006

2,010

0.004

0.617

Teso South

0.004

4,300

0.0045

0.638

Kwale

0.017

2,060

0.010

0.597

Galole

0.002

2,280

0.0011

0.622

Kilifi

0.029

2,870

0.024

0.565

Bura

0.002

2,040

0.0013

0.616

Lamu

0.003

4,190

0.003

0.471

Garsen

0.003

1,810

0.0014

0.608

Migori

0.024

3,450

0.024

0.464

Magarini

0.005

1,450

0.0019

0.608

Busia

0.020

2,560

0.015

0.459

Kinango

0.006

1,210

0.0019

0.575

Taita-Taveta

0.007

2,850

0.006

0.437

Kilifi North

0.005

3,250

0.0051

0.550

Garissa

0.011

2,640

0.009

0.436

Lunga Lunga

0.004

1,270

0.0015

0.544

Isiolo

0.005

3,030

0.004

0.431

Malindi

0.004

4,510

0.0056

0.540

Bungoma

0.036

3,020

0.032

0.430

Kaloleni

0.004

2,750

0.0033

0.539

Median County

Median constituency

Kericho

0.020

3,300

0.019

0.378

Samburu West

0.002

2,010

0.0013

0.344

Bottom 10 counties

Bottom 10 constituencies

Nandi

0.020

2,820

0.016

0.343

Emurua Dikirr

0.003

2,040

0.0015

0.263

Nairobi

0.082

7,230

0.172

0.341

Laisamis

0.002

1,430

0.0007

0.252

Bomet

0.019

2,390

0.013

0.338

Kacheliba

0.004

1,410

0.0014

0.246

Kiambu

0.043

5,050

0.063

0.335

Banissa

0.004

1,140

0.0014

0.241

Samburu

0.006

1,920

0.003

0.332

Tiaty

0.004

1,610

0.0017

0.234

Mandera

0.025

1,400

0.010

0.332

North Horr

0.002

1,330

0.0008

0.214

Wajir

0.014

1,320

0.005

0.321

Turkana South

0.004

1,210

0.0013

0.211

West Pokot

0.013

1,900

0.007

0.318

Loima

0.003

1,110

0.0010

0.185

Narok

0.022

2,510

0.016

0.315

Turkana North

0.004

1,150

0.0013

0.173

Turkana

0.021

1,380

0.009

0.283

Turkana East

0.002

1,150

0.0008

0.169

The analysis from this chapter indicates that income inequality is somewhat higher among rural households than urban households. The reduction in national income inequality can be achieved through addressing the significant income differentials between urban and rural areas. At county level, the most unequal counties are not the poorest, but they do tend to be unequal in both rural and urban areas. Whereas counties are mandated to collect some revenues locally, taxation of the rich to reduce the poverty gap in the affected counties will remain a big challenge. This supports the notion of a set of transfers from national government that are redistributive and that target inequalities across and within counties.